



from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFLEDForConditionalGeneration, TFLEDModel






@require_tf
class SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






    __lowerCamelCase      :			Any =				LEDConfig
    __lowerCamelCase      :			Optional[Any] =				{}
    __lowerCamelCase      :			Any =				"gelu"






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__=13,		lowerCamelCase__=7,		lowerCamelCase__=True,		lowerCamelCase__=False,		lowerCamelCase__=99,		lowerCamelCase__=32,		lowerCamelCase__=2,		lowerCamelCase__=4,		lowerCamelCase__=37,		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=20,		lowerCamelCase__=2,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=4,		):
        A			:	Union[str, Any]										=       parent
        A			:	List[str]										=       batch_size
        A			:	List[str]										=       seq_length
        A			:	List[str]										=       is_training
        A			:	int										=       use_labels
        A			:	Dict										=       vocab_size
        A			:	Optional[int]										=       hidden_size
        A			:	Dict										=       num_hidden_layers
        A			:	Any										=       num_attention_heads
        A			:	Tuple										=       intermediate_size

        A			:	Optional[int]										=       hidden_dropout_prob
        A			:	int										=       attention_probs_dropout_prob
        A			:	Optional[Any]										=       max_position_embeddings
        A			:	Tuple										=       eos_token_id
        A			:	Dict										=       pad_token_id
        A			:	str										=       bos_token_id
        A			:	Any										=       attention_window

        # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
        # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
        # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
        # because its local attention only attends to `self.attention_window` and one before and one after
        A			:	Any										=       self.attention_window + 2

        # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
        # the `test_attention_outputs` and `test_hidden_states_output` tests
        A			:	Union[str, Any]										=       (
            self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
        )






    def       _lowerCAmelCase (	self		):
        A			:	int										=       ids_tensor([self.batch_size, self.seq_length - 1],		self.vocab_size		)
        A			:	Any										=       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		),		1		)
        A			:	Any										=       tf.concat([input_ids, eos_tensor],		axis=1		)

        A			:	str										=       ids_tensor([self.batch_size, self.seq_length],		self.vocab_size		)

        A			:	Any										=       self.config_cls(
            vocab_size=self.vocab_size,		d_model=self.hidden_size,		encoder_layers=self.num_hidden_layers,		decoder_layers=self.num_hidden_layers,		encoder_attention_heads=self.num_attention_heads,		decoder_attention_heads=self.num_attention_heads,		encoder_ffn_dim=self.intermediate_size,		decoder_ffn_dim=self.intermediate_size,		dropout=self.hidden_dropout_prob,		attention_dropout=self.attention_probs_dropout_prob,		max_position_embeddings=self.max_position_embeddings,		eos_token_ids=[2],		bos_token_id=self.bos_token_id,		pad_token_id=self.pad_token_id,		decoder_start_token_id=self.pad_token_id,		attention_window=self.attention_window,		**self.config_updates,		)
        A			:	int										=       prepare_led_inputs_dict(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        A			:	int										=       tf.concat(
            [tf.zeros_like(lowerCamelCase__		)[:, :-1], tf.ones_like(lowerCamelCase__		)[:, -1:]],		axis=-1,		)
        A			:	str										=       global_attention_mask
        return config, inputs_dict






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Union[str, Any]										=       TFLEDModel(config=lowerCamelCase__		).get_decoder()
        A			:	Any										=       inputs_dict["""input_ids"""]

        A			:	List[str]										=       input_ids[:1, :]
        A			:	Union[str, Any]										=       inputs_dict["""attention_mask"""][:1, :]
        A			:	Any										=       1

        # first forward pass
        A			:	List[str]										=       model(lowerCamelCase__,		attention_mask=lowerCamelCase__,		use_cache=lowerCamelCase__		)

        A			:	str										=       outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        A			:	List[Any]										=       ids_tensor((self.batch_size, 3),		config.vocab_size		)
        A			:	List[str]										=       tf.cast(ids_tensor((self.batch_size, 3),		2		),		tf.inta		)

        # append to next input_ids and
        A			:	Union[str, Any]										=       tf.concat([input_ids, next_tokens],		axis=-1		)
        A			:	str										=       tf.concat([attention_mask, next_attn_mask],		axis=-1		)

        A			:	List[str]										=       model(lowerCamelCase__,		attention_mask=lowerCamelCase__		)[0]
        A			:	List[Any]										=       model(lowerCamelCase__,		attention_mask=lowerCamelCase__,		past_key_values=lowerCamelCase__		)[0]

        self.parent.assertEqual(next_tokens.shape[1],		output_from_past.shape[1]		)

        # select random slice
        A			:	Union[str, Any]										=       int(ids_tensor((1,),		output_from_past.shape[-1]		)		)
        A			:	Union[str, Any]										=       output_from_no_past[:, -3:, random_slice_idx]
        A			:	Dict										=       output_from_past[:, :, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(lowerCamelCase__,		lowerCamelCase__,		rtol=1e-3		)




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase=None				,					_lowerCAmelCase=None				,					_lowerCAmelCase=None				,					_lowerCAmelCase=None				,					)	->  str:



    """simple docstring"""






    if attention_mask is None:
        A			:	Tuple										=       tf.cast(tf.math.not_equal(_lowerCAmelCase				,					config.pad_token_id		)				,					tf.inta		)
    if decoder_attention_mask is None:
        A			:	Optional[int]										=       tf.concat(
            [
                tf.ones(decoder_input_ids[:, :1].shape				,					dtype=tf.inta		),
                tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]				,					config.pad_token_id		)				,					tf.inta		),
            ]				,					axis=-1				,					)
    if head_mask is None:
        A			:	Dict										=       tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
    if decoder_head_mask is None:
        A			:	Dict										=       tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
    return {
        "input_ids": input_ids,
        "attention_mask": attention_mask,
        "decoder_input_ids": decoder_input_ids,
        "decoder_attention_mask": decoder_attention_mask,
        "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask,
    }






@require_tf
class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__			,						unittest.TestCase   ):




    '''simple docstring'''






    __lowerCamelCase      :			Any =				(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
    __lowerCamelCase      :			Dict =				(TFLEDForConditionalGeneration,) if is_tf_available() else ()
    __lowerCamelCase      :			Dict =				(
        {
            "conversational": TFLEDForConditionalGeneration,
            "feature-extraction": TFLEDModel,
            "summarization": TFLEDForConditionalGeneration,
            "text2text-generation": TFLEDForConditionalGeneration,
            "translation": TFLEDForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    __lowerCamelCase      :			List[str] =				True
    __lowerCamelCase      :			Dict =				False
    __lowerCamelCase      :			str =				False
    __lowerCamelCase      :			Union[str, Any] =				False






    def       _lowerCAmelCase (	self		):
        A			:	List[Any]										=       TFLEDModelTester(self		)
        A			:	Tuple										=       ConfigTester(self,		config_class=lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        self.config_tester.run_common_tests()






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        A			:	Union[str, Any]										=       self.model_tester.prepare_config_and_inputs_for_common()
        A			:	str										=       tf.zeros_like(inputs_dict["""attention_mask"""]		)
        A			:	Any										=       2
        A			:	Optional[int]										=       tf.where(
            tf.range(self.model_tester.seq_length		)[None, :] < num_global_attn_indices,		1,		inputs_dict["""global_attention_mask"""],		)

        A			:	Tuple										=       True
        A			:	int										=       self.model_tester.seq_length
        A			:	Any										=       self.model_tester.encoder_seq_length

        def check_decoder_attentions_output(lowerCamelCase__		):
            A			:	int										=       outputs.decoder_attentions
            self.assertEqual(len(lowerCamelCase__		),		self.model_tester.num_hidden_layers		)
            self.assertListEqual(
                list(decoder_attentions[0].shape[-3:]		),		[self.model_tester.num_attention_heads, seq_length, seq_length],		)

        def check_encoder_attentions_output(lowerCamelCase__		):
            A			:	str										=       [t.numpy() for t in outputs.encoder_attentions]
            A			:	Optional[int]										=       [t.numpy() for t in outputs.encoder_global_attentions]
            self.assertEqual(len(lowerCamelCase__		),		self.model_tester.num_hidden_layers		)
            self.assertEqual(len(lowerCamelCase__		),		self.model_tester.num_hidden_layers		)
            self.assertListEqual(
                list(attentions[0].shape[-3:]		),		[self.model_tester.num_attention_heads, seq_length, seq_length],		)
            self.assertListEqual(
                list(global_attentions[0].shape[-3:]		),		[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices],		)

        for model_class in self.all_model_classes:
            A			:	Union[str, Any]										=       True
            A			:	Optional[int]										=       False
            A			:	Tuple										=       False
            A			:	str										=       model_class(lowerCamelCase__		)
            A			:	Optional[Any]										=       model(self._prepare_for_class(lowerCamelCase__,		lowerCamelCase__		)		)
            A			:	Optional[int]										=       len(lowerCamelCase__		)
            self.assertEqual(config.output_hidden_states,		lowerCamelCase__		)
            check_encoder_attentions_output(lowerCamelCase__		)

            if self.is_encoder_decoder:
                A			:	Tuple										=       model_class(lowerCamelCase__		)
                A			:	Any										=       model(self._prepare_for_class(lowerCamelCase__,		lowerCamelCase__		)		)
                self.assertEqual(config.output_hidden_states,		lowerCamelCase__		)
                check_decoder_attentions_output(lowerCamelCase__		)

            # Check that output attentions can also be changed via the config
            del inputs_dict["output_attentions"]
            A			:	Dict										=       True
            A			:	str										=       model_class(lowerCamelCase__		)
            A			:	Tuple										=       model(self._prepare_for_class(lowerCamelCase__,		lowerCamelCase__		)		)
            self.assertEqual(config.output_hidden_states,		lowerCamelCase__		)
            check_encoder_attentions_output(lowerCamelCase__		)

            # Check attention is always last and order is fine
            A			:	Tuple										=       True
            A			:	Dict										=       True
            A			:	List[str]										=       model_class(lowerCamelCase__		)
            A			:	Tuple										=       model(self._prepare_for_class(lowerCamelCase__,		lowerCamelCase__		)		)

            self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1),		len(lowerCamelCase__		)		)
            self.assertEqual(model.config.output_hidden_states,		lowerCamelCase__		)
            check_encoder_attentions_output(lowerCamelCase__		)






    @unittest.skip("""LED keeps using potentially symbolic tensors in conditionals and breaks tracing."""		)
    def       _lowerCAmelCase (	self		):
        pass






    def       _lowerCAmelCase (	self		):
        # TODO: Head-masking not yet implement
        pass




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Optional[Any]:



    """simple docstring"""






    return tf.constant(_lowerCAmelCase				,					dtype=tf.intaa		)


SCREAMING_SNAKE_CASE_:List[str]					=						1E-4






@slow
@require_tf
class SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):




    '''simple docstring'''






    def       _lowerCAmelCase (	self		):
        A			:	Optional[int]										=       TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""		).led

        # change to intended input here
        A			:	Any										=       _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]		)
        A			:	Union[str, Any]										=       _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]		)
        A			:	Tuple										=       prepare_led_inputs_dict(model.config,		lowerCamelCase__,		lowerCamelCase__		)
        A			:	Optional[Any]										=       model(**lowerCamelCase__		)[0]
        A			:	Union[str, Any]										=       (1, 1024, 768)
        self.assertEqual(output.shape,		lowerCamelCase__		)
        # change to expected output here
        A			:	str										=       tf.convert_to_tensor(
            [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]],		)
        tf.debugging.assert_near(output[:, :3, :3],		lowerCamelCase__,		atol=1e-3		)






    def       _lowerCAmelCase (	self		):
        A			:	Union[str, Any]										=       TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""		)

        # change to intended input here
        A			:	Dict										=       _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]		)
        A			:	Dict										=       _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]		)
        A			:	Optional[Any]										=       prepare_led_inputs_dict(model.config,		lowerCamelCase__,		lowerCamelCase__		)
        A			:	Dict										=       model(**lowerCamelCase__		)[0]
        A			:	List[Any]										=       (1, 1024, model.config.vocab_size)
        self.assertEqual(output.shape,		lowerCamelCase__		)
        # change to expected output here
        A			:	Dict										=       tf.convert_to_tensor(
            [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]],		)
        tf.debugging.assert_near(output[:, :3, :3],		lowerCamelCase__,		atol=1e-3,		rtol=1e-3		)







import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  str:



    """simple docstring"""






    A			:	int										=       []
    for line in lines:
        A			:	int										=       re.sub(R"""#.*"""				,					""""""				,					_lowerCAmelCase		)  # remove comments
        if line:
            filtered_lines.append(_lowerCAmelCase		)
    A			:	Tuple										=       """\n""".join(_lowerCAmelCase		)

    # Make a hash from all this code
    A			:	Union[str, Any]										=       full_str.encode("""utf-8"""		)
    return shaaaa(_lowerCAmelCase		).hexdigest()


# get importable module names and hash for caching
SCREAMING_SNAKE_CASE_:List[Any]					=						{
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
SCREAMING_SNAKE_CASE_:Optional[Any]					=						{
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
SCREAMING_SNAKE_CASE_:Optional[int]					=						{"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
SCREAMING_SNAKE_CASE_:Dict[str, List[str]]					=						{}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
     _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")




_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")


