


'''simple docstring'''



import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


_lowercase      :      Tuple            =					logging.get_logger(__name__)

_lowercase      :      Tuple            =					{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowercase      :      Dict            =					{
    """vocab_file""": {
        """facebook/dpr-ctx_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-ctx_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-ctx_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-ctx_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}
_lowercase      :      Any            =					{
    """vocab_file""": {
        """facebook/dpr-question_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-question_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-question_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-question_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}
_lowercase      :      Optional[int]            =					{
    """vocab_file""": {
        """facebook/dpr-reader-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-reader-multiset-base""": (
            """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-reader-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-reader-multiset-base""": (
            """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}

_lowercase      :      Dict            =					{
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
_lowercase      :      str            =					{
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
_lowercase      :      Optional[int]            =					{
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


_lowercase      :      Optional[Any]            =					{
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
_lowercase      :      List[Any]            =					{
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
_lowercase      :      Optional[int]            =					{
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				Any  		=	VOCAB_FILES_NAMES
				__magic_name__    :				Optional[Any]  		=	CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				__magic_name__    :				Optional[int]  		=	CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__magic_name__    :				Union[str, Any]  		=	CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
				__magic_name__    :				Any  		=	DPRContextEncoderTokenizer







class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				List[str]  		=	VOCAB_FILES_NAMES
				__magic_name__    :				Union[str, Any]  		=	QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				__magic_name__    :				Optional[int]  		=	QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__magic_name__    :				Tuple  		=	QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
				__magic_name__    :				Union[str, Any]  		=	DPRQuestionEncoderTokenizer







_lowercase      :      List[Any]            =					collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

_lowercase      :      List[str]            =					collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


_lowercase      :      int            =					r"""
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer's default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Return:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    """






@add_start_docstrings(lowerCAmelCase	)
class 	UpperCamelCase__:




				def __call__(		self      :			Dict				,   lowerCAmelCase      :			Optional[Any]				,   lowerCAmelCase      :			Optional[str] = None				,   lowerCAmelCase      :			Optional[str] = None				,   lowerCAmelCase      :			Union[bool, str] = False				,   lowerCAmelCase      :			Union[bool, str] = False				,   lowerCAmelCase      :			Optional[int] = None				,   lowerCAmelCase      :			Optional[Union[str, TensorType]] = None				,   lowerCAmelCase      :			Optional[bool] = None				,   **lowerCAmelCase      :			Union[str, Any]				,   )->					BatchEncoding:

										"""simple docstring"""




										if titles is None and texts is None:
																return super().__call__(
																    lowerCAmelCase				,   padding=lowerCAmelCase				,   truncation=lowerCAmelCase				,   max_length=lowerCAmelCase				,   return_tensors=lowerCAmelCase				,   return_attention_mask=lowerCAmelCase				,   **lowerCAmelCase				,   )
										elif titles is None or texts is None:
																UpperCAmelCase					    =     titles if texts is None else texts
																return super().__call__(
																    lowerCAmelCase				,   lowerCAmelCase				,   padding=lowerCAmelCase				,   truncation=lowerCAmelCase				,   max_length=lowerCAmelCase				,   return_tensors=lowerCAmelCase				,   return_attention_mask=lowerCAmelCase				,   **lowerCAmelCase				,   )
										UpperCAmelCase					    =     titles if not isinstance(lowerCAmelCase				,   lowerCAmelCase				) else [titles]
										UpperCAmelCase					    =     texts if not isinstance(lowerCAmelCase				,   lowerCAmelCase				) else [texts]
										UpperCAmelCase					    =     len(lowerCAmelCase				)
										UpperCAmelCase					    =     questions if not isinstance(lowerCAmelCase				,   lowerCAmelCase				) else [questions] * n_passages
										assert len(lowerCAmelCase				) == len(
										    lowerCAmelCase				), F"""There should be as many titles than texts but got {len(lowerCAmelCase				)} titles and {len(lowerCAmelCase				)} texts."""
										UpperCAmelCase					    =     super().__call__(lowerCAmelCase				,   lowerCAmelCase				,   padding=lowerCAmelCase				,   truncation=lowerCAmelCase				)['''input_ids''']
										UpperCAmelCase					    =     super().__call__(lowerCAmelCase				,   add_special_tokens=lowerCAmelCase				,   padding=lowerCAmelCase				,   truncation=lowerCAmelCase				)['''input_ids''']
										UpperCAmelCase					    =     {
										    '''input_ids''': [
										        (encoded_question_and_title + encoded_text)[:max_length]
										        if max_length is not None and truncation
										        else encoded_question_and_title + encoded_text
										        for encoded_question_and_title, encoded_text in zip(lowerCAmelCase				,   lowerCAmelCase				)
										    ]
										}
										if return_attention_mask is not False:
																UpperCAmelCase					    =     []
																for input_ids in encoded_inputs["input_ids"]:
																						attention_mask.append([int(input_id != self.pad_token_id				) for input_id in input_ids]				)
																UpperCAmelCase					    =     attention_mask
										return self.pad(lowerCAmelCase				,   padding=lowerCAmelCase				,   max_length=lowerCAmelCase				,   return_tensors=lowerCAmelCase				)




				def 					a__(		self      :			Optional[int]				,   lowerCAmelCase      :			BatchEncoding				,   lowerCAmelCase      :			DPRReaderOutput				,   lowerCAmelCase      :			int = 16				,   lowerCAmelCase      :			int = 64				,   lowerCAmelCase      :			int = 4				,   )->					List[DPRSpanPrediction]:

										"""simple docstring"""




										UpperCAmelCase					    =     reader_input['''input_ids''']
										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     reader_output[:3]
										UpperCAmelCase					    =     len(lowerCAmelCase				)
										UpperCAmelCase					    =     sorted(range(lowerCAmelCase				)				,   reverse=lowerCAmelCase				,   key=relevance_logits.__getitem__				)
										UpperCAmelCase					    =     []
										for doc_id in sorted_docs:
																UpperCAmelCase					    =     list(input_ids[doc_id]				)
																# assuming question & title information is at the beginning of the sequence
																UpperCAmelCase					    =     sequence_ids.index(self.sep_token_id				,   2				) + 1  # second sep id
																if sequence_ids[-1] == self.pad_token_id:
																						UpperCAmelCase					    =     sequence_ids.index(self.pad_token_id				)
																else:
																						UpperCAmelCase					    =     len(lowerCAmelCase				)

																UpperCAmelCase					    =     self._get_best_spans(
																    start_logits=start_logits[doc_id][passage_offset:sequence_len]				,   end_logits=end_logits[doc_id][passage_offset:sequence_len]				,   max_answer_length=lowerCAmelCase				,   top_spans=lowerCAmelCase				,   )
																for start_index, end_index in best_spans:
																						start_index += passage_offset
																						end_index += passage_offset
																						nbest_spans_predictions.append(
																						    DPRSpanPrediction(
																						        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]				,   relevance_score=relevance_logits[doc_id]				,   doc_id=lowerCAmelCase				,   start_index=lowerCAmelCase				,   end_index=lowerCAmelCase				,   text=self.decode(sequence_ids[start_index : end_index + 1]				)				,   )				)
																if len(lowerCAmelCase				) >= num_spans:
																						break
										return nbest_spans_predictions[:num_spans]





				def 					a__(		self      :			Any				,   lowerCAmelCase      :			List[int]				,   lowerCAmelCase      :			List[int]				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			int				,   )->					List[DPRSpanPrediction]:

										"""simple docstring"""




										UpperCAmelCase					    =     []
										for start_index, start_score in enumerate(lowerCAmelCase				):
																for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]				):
																						scores.append(((start_index, start_index + answer_length), start_score + end_score)				)
										UpperCAmelCase					    =     sorted(lowerCAmelCase				,   key=lambda lowerCAmelCase				: x[1]				,   reverse=lowerCAmelCase				)
										UpperCAmelCase					    =     []
										for (start_index, end_index), score in scores:
																assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
																UpperCAmelCase					    =     end_index - start_index + 1
																assert length <= max_answer_length, F"""Span is too long: {length} > {max_answer_length}"""
																if any(
																    start_index <= prev_start_index <= prev_end_index <= end_index
																    or prev_start_index <= start_index <= end_index <= prev_end_index
																    for (prev_start_index, prev_end_index) in chosen_span_intervals				):
																						continue
																chosen_span_intervals.append((start_index, end_index)				)

																if len(lowerCAmelCase				) == top_spans:
																						break
										return chosen_span_intervals







@add_end_docstrings(lowerCAmelCase	)
class 	UpperCamelCase__(       lowerCAmelCase						,    lowerCAmelCase	):
				__magic_name__    :				Tuple  		=	VOCAB_FILES_NAMES
				__magic_name__    :				List[Any]  		=	READER_PRETRAINED_VOCAB_FILES_MAP
				__magic_name__    :				Tuple  		=	READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__magic_name__    :				Any  		=	READER_PRETRAINED_INIT_CONFIGURATION
				__magic_name__    :				List[str]  		=	["input_ids", "attention_mask"]
				__magic_name__    :				str  		=	DPRReaderTokenizer









'''simple docstring'''



import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
	import os

	import jax.numpy as jnp
	from jax import jit

	from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
	from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

	_lowercase      :      List[str]            =					"""0.12"""  # assumed parallelism: 8


if is_torch_available():
	import torch


def 				lowerCamelCase__				(	A       :      str				,       A       :      str				,       A       :      List[Any]=None					):




						'''simple docstring'''






						if rng is None:
												UpperCAmelCase					    =     random.Random()

						UpperCAmelCase					    =     1
						for dim in shape:
												total_dims *= dim

						UpperCAmelCase					    =     []
						for _ in range(A					):
												values.append(rng.randint(0				,       vocab_size - 1					)					)

						UpperCAmelCase					    =     np.array(A				,       dtype=jnp.intaa					).reshape(A					)

						return output


def 				lowerCamelCase__				(	A       :      int				,       A       :      Optional[int]=None					):




						'''simple docstring'''






						UpperCAmelCase					    =     ids_tensor(A				,       vocab_size=2				,       rng=A					)
						# make sure that at least one token is attended to for each batch
						UpperCAmelCase					    =     1
						return attn_mask






@require_flax
class 	UpperCamelCase__:
				__magic_name__    :				Optional[int]  		=	None
				__magic_name__    :				Optional[Any]  		=	()




				def 					a__(		self      :			str				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()

										# cut to half length & take max batch_size 3
										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     inputs['''input_ids'''].shape[-1] // 2
										UpperCAmelCase					    =     inputs['''input_ids'''][:max_batch_size, :sequence_length]

										UpperCAmelCase					    =     jnp.ones_like(lowerCAmelCase				)
										UpperCAmelCase					    =     attention_mask[:max_batch_size, :sequence_length]

										# generate max 5 tokens
										UpperCAmelCase					    =     input_ids.shape[-1] + 5
										if config.eos_token_id is not None and config.pad_token_id is None:
																# hack to allow generate for models such as GPT2 as is done in `generate()`
																UpperCAmelCase					    =     config.eos_token_id
										return config, input_ids, attention_mask, max_length




				@is_pt_flax_cross_test
				def 					a__(		self      :			Dict				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     0

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model_class.__name__[4:]  # Skip the "Flax" at the beginning
																UpperCAmelCase					    =     getattr(lowerCAmelCase				,   lowerCAmelCase				)
																UpperCAmelCase					    =     pt_model_class(lowerCAmelCase				).eval()
																UpperCAmelCase					    =     load_flax_weights_in_pytorch_model(lowerCAmelCase				,   flax_model.params				)

																UpperCAmelCase					    =     flax_model.generate(lowerCAmelCase				).sequences
																UpperCAmelCase					    =     pt_model.generate(torch.tensor(lowerCAmelCase				,   dtype=torch.long				)				)

																if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																						UpperCAmelCase					    =     flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

																self.assertListEqual(pt_generation_outputs.numpy().tolist()				,   flax_generation_outputs.tolist()				)




				def 					a__(		self      :			Any				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			Optional[Any]				)->					int:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			str				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     2

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			List[Any]				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     2

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[0]				,   input_ids.shape[0] * config.num_return_sequences				)




				def 					a__(		self      :			Tuple				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     0.8
										UpperCAmelCase					    =     10
										UpperCAmelCase					    =     0.3
										UpperCAmelCase					    =     1
										UpperCAmelCase					    =     8
										UpperCAmelCase					    =     9

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			Optional[Any]				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     1
										UpperCAmelCase					    =     8
										UpperCAmelCase					    =     9

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			Tuple				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     1
										UpperCAmelCase					    =     8
										UpperCAmelCase					    =     9

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			Union[str, Any]				)->					Any:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()

										# pad attention mask on the left
										UpperCAmelCase					    =     attention_mask.at[(0, 0)].set(0				)

										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			Optional[Any]				)->					int:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()

										# pad attention mask on the left
										UpperCAmelCase					    =     attention_mask.at[(0, 0)].set(0				)

										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)





				def 					a__(		self      :			Tuple				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()

										# pad attention mask on the left
										UpperCAmelCase					    =     attention_mask.at[(0, 0)].set(0				)

										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)







@require_flax
class 	UpperCamelCase__(       unittest.TestCase	):




				def 					a__(		self      :			Union[str, Any]				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''				)
										UpperCAmelCase					    =     FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''				)

										UpperCAmelCase					    =     '''Hello world'''
										UpperCAmelCase					    =     tokenizer(lowerCAmelCase				,   return_tensors='''np'''				).input_ids

										# typos are quickly detected (the correct argument is `do_sample`)
										with self.assertRaisesRegex(lowerCAmelCase				,   '''do_samples'''				):
																model.generate(lowerCAmelCase				,   do_samples=lowerCAmelCase				)

										# arbitrary arguments that will not be used anywhere are also not accepted
										with self.assertRaisesRegex(lowerCAmelCase				,   '''foo'''				):
																UpperCAmelCase					    =     {'''foo''': '''bar'''}
																model.generate(lowerCAmelCase				,   **lowerCAmelCase				)





