



from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
     import tensorflow as tf

     from transformers.models.xglm.modeling_tf_xglm import (
         TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFXGLMForCausalLM,
         TFXGLMModel,
     )






@require_tf
class SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






    __lowerCamelCase      :			Dict =				XGLMConfig
    __lowerCamelCase      :			Dict =				{}
    __lowerCamelCase      :			str =				'''gelu'''






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__=14,		lowerCamelCase__=7,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=99,		lowerCamelCase__=32,		lowerCamelCase__=2,		lowerCamelCase__=4,		lowerCamelCase__=37,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=0.02,		):
        A			:	int										=       parent
        A			:	Any										=       batch_size
        A			:	List[Any]										=       seq_length
        A			:	Union[str, Any]										=       is_training
        A			:	Tuple										=       use_input_mask
        A			:	Any										=       use_labels
        A			:	Tuple										=       vocab_size
        A			:	Dict										=       d_model
        A			:	Union[str, Any]										=       num_hidden_layers
        A			:	Union[str, Any]										=       num_attention_heads
        A			:	Tuple										=       ffn_dim
        A			:	Optional[Any]										=       activation_function
        A			:	str										=       activation_dropout
        A			:	Union[str, Any]										=       attention_dropout
        A			:	str										=       max_position_embeddings
        A			:	Union[str, Any]										=       initializer_range
        A			:	List[Any]										=       None
        A			:	List[Any]										=       0
        A			:	Dict										=       2
        A			:	Dict										=       1






    def       _lowerCAmelCase (	self		):
        return XGLMConfig.from_pretrained("""facebook/xglm-564M"""		)






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       tf.clip_by_value(
            ids_tensor([self.batch_size, self.seq_length],		self.vocab_size		),		clip_value_min=0,		clip_value_max=3		)

        A			:	Union[str, Any]										=       None
        if self.use_input_mask:
            A			:	Any										=       random_attention_mask([self.batch_size, self.seq_length]		)

        A			:	List[str]										=       self.get_config()

        A			:	Tuple										=       floats_tensor([self.num_hidden_layers, self.num_attention_heads],		2		)

        return (
            config,
            input_ids,
            input_mask,
            head_mask,
        )






    def       _lowerCAmelCase (	self		):
        return XGLMConfig(
            vocab_size=self.vocab_size,		d_model=self.hidden_size,		num_layers=self.num_hidden_layers,		attention_heads=self.num_attention_heads,		ffn_dim=self.ffn_dim,		activation_function=self.activation_function,		activation_dropout=self.activation_dropout,		attention_dropout=self.attention_dropout,		max_position_embeddings=self.max_position_embeddings,		initializer_range=self.initializer_range,		use_cache=_a,		bos_token_id=self.bos_token_id,		eos_token_id=self.eos_token_id,		pad_token_id=self.pad_token_id,		return_dict=_a,		)






    def       _lowerCAmelCase (	self		):
        A			:	Optional[Any]										=       self.prepare_config_and_inputs()

        (
            (
            A				
        )					,							(
            A				
        )					,							(
            A				
        )					,							(
            A				
        )					,											
        )			:	Optional[Any]										=       config_and_inputs

        A			:	Dict										=       {
            """input_ids""": input_ids,
            """head_mask""": head_mask,
        }

        return config, inputs_dict






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




    '''simple docstring'''






    __lowerCamelCase      :			Optional[int] =				(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
    __lowerCamelCase      :			Optional[Any] =				(TFXGLMForCausalLM,) if is_tf_available() else ()
    __lowerCamelCase      :			str =				(
        {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
    )
    __lowerCamelCase      :			str =				False
    __lowerCamelCase      :			str =				False
    __lowerCamelCase      :			Dict =				False






    def       _lowerCAmelCase (	self		):
        A			:	int										=       TFXGLMModelTester(self		)
        A			:	List[str]										=       ConfigTester(self,		config_class=_a,		n_embd=37		)






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






    @slow
    def       _lowerCAmelCase (	self		):
        for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A			:	int										=       TFXGLMModel.from_pretrained(_a		)
            self.assertIsNotNone(_a		)






    @unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""		)
    def       _lowerCAmelCase (	self		):
        super().test_resize_token_embeddings()






@require_tf
class SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):




    '''simple docstring'''






    @slow
    def       _lowerCAmelCase (	self,		lowerCamelCase__=True		):
        A			:	Optional[Any]										=       TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""		)
        A			:	str										=       tf.convert_to_tensor([[2, 268, 9865]],		dtype=tf.intaa		)  # The dog
        # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
        # fmt: off
        A			:	Any										=       [2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
        # fmt: on
        A			:	Optional[Any]										=       model.generate(_a,		do_sample=_a,		num_beams=1		)
        if verify_outputs:
            self.assertListEqual(output_ids[0].numpy().tolist(),		_a		)






    @slow
    def       _lowerCAmelCase (	self		):
        A			:	List[str]										=       XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""		)
        A			:	Optional[int]										=       TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""		)

        tf.random.set_seed(0		)
        A			:	Optional[int]										=       tokenizer("""Today is a nice day and""",		return_tensors="""tf"""		)
        A			:	int										=       tokenized.input_ids
        # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
        with tf.device(""":/CPU:0"""		):
            A			:	Dict										=       model.generate(_a,		do_sample=_a,		seed=[7, 0]		)
        A			:	List[Any]										=       tokenizer.decode(output_ids[0],		skip_special_tokens=_a		)

        A			:	List[str]										=       (
            """Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"""
        )
        self.assertEqual(_a,		_a		)






    @slow
    def       _lowerCAmelCase (	self		):
        A			:	Any										=       TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""		)
        A			:	Union[str, Any]										=       XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""		)

        A			:	Dict										=       """left"""

        # use different length sentences to test batching
        A			:	Tuple										=       [
            """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
            """left-padding, such as in batched generation. The output for the sequence below should be the same """
            """regardless of whether left padding is applied or not. When""",
            """Hello, my dog is a little""",
        ]

        A			:	List[Any]										=       tokenizer(_a,		return_tensors="""tf""",		padding=_a		)
        A			:	Optional[Any]										=       inputs["""input_ids"""]

        A			:	List[str]										=       model.generate(input_ids=_a,		attention_mask=inputs["""attention_mask"""],		max_new_tokens=12		)

        A			:	List[Any]										=       tokenizer(sentences[0],		return_tensors="""tf"""		).input_ids
        A			:	Optional[int]										=       model.generate(input_ids=_a,		max_new_tokens=12		)

        A			:	int										=       tokenizer(sentences[1],		return_tensors="""tf"""		).input_ids
        A			:	List[Any]										=       model.generate(input_ids=_a,		max_new_tokens=12		)

        A			:	Any										=       tokenizer.batch_decode(_a,		skip_special_tokens=_a		)
        A			:	Union[str, Any]										=       tokenizer.decode(output_non_padded[0],		skip_special_tokens=_a		)
        A			:	List[Any]										=       tokenizer.decode(output_padded[0],		skip_special_tokens=_a		)

        A			:	Tuple										=       [
            """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
            """left-padding, such as in batched generation. The output for the sequence below should be the same """
            """regardless of whether left padding is applied or not. When left padding is applied, the sequence will be """
            """a single""",
            """Hello, my dog is a little bit of a shy one, but he is very friendly""",
        ]
        self.assertListEqual(_a,		_a		)
        self.assertListEqual(_a,		[non_padded_sentence, padded_sentence]		)







from argparse import ArgumentParser

from .env import EnvironmentCommand




def      __UpperCamelCase     (  )	->  Dict:



    """simple docstring"""






    A			:	str										=       ArgumentParser("""Diffusers CLI tool"""				,					usage="""diffusers-cli <command> [<args>]"""		)
    A			:	int										=       parser.add_subparsers(help="""diffusers-cli command helpers"""		)

    # Register commands
    EnvironmentCommand.register_subcommand(_lowerCAmelCase		)

    # Let's go
    A			:	str										=       parser.parse_args()

    if not hasattr(_lowerCAmelCase				,					"""func"""		):
        parser.print_help()
        exit(1		)

    # Run
    A			:	Any										=       args.func(_lowerCAmelCase		)
    service.run()


if __name__ == "__main__":
     main()


