
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        lowercase      :
		__lowercase : Union[str, Any]			  =       XGLMConfig
		__lowercase : Dict			  =       {}
		__lowercase : str			  =       "gelu"



		def __init__(				self       ,   A_       ,   A_=14       ,   A_=7       ,   A_=True       ,   A_=True       ,   A_=True       ,   A_=99       ,   A_=32       ,   A_=2       ,   A_=4       ,   A_=37       ,   A_="gelu"       ,   A_=0.1       ,   A_=0.1       ,   A_=512       ,   A_=0.02       ,   )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  parent
				UpperCamelCase						=  batch_size
				UpperCamelCase						=  seq_length
				UpperCamelCase						=  is_training
				UpperCamelCase						=  use_input_mask
				UpperCamelCase						=  use_labels
				UpperCamelCase						=  vocab_size
				UpperCamelCase						=  d_model
				UpperCamelCase						=  num_hidden_layers
				UpperCamelCase						=  num_attention_heads
				UpperCamelCase						=  ffn_dim
				UpperCamelCase						=  activation_function
				UpperCamelCase						=  activation_dropout
				UpperCamelCase						=  attention_dropout
				UpperCamelCase						=  max_position_embeddings
				UpperCamelCase						=  initializer_range
				UpperCamelCase						=  None
				UpperCamelCase						=  0
				UpperCamelCase						=  2
				UpperCamelCase						=  1



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				return XGLMConfig.from_pretrained('facebook/xglm-564M' )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  tf.clip_by_value(
				    ids_tensor([self.batch_size, self.seq_length]       ,   self.vocab_size )       ,   clip_value_min=0       ,   clip_value_max=3 )

				UpperCamelCase						=  None
				if self.use_input_mask:
						UpperCamelCase						=  random_attention_mask([self.batch_size, self.seq_length] )

				UpperCamelCase						=  self.get_config()

				UpperCamelCase						=  floats_tensor([self.num_hidden_layers, self.num_attention_heads]       ,   2 )

				return (
				    config,
				    input_ids,
				    input_mask,
				    head_mask,
				)



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				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 __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  self.prepare_config_and_inputs()

				(
				    (
				    UpperCamelCase
				)			,						(
				    UpperCamelCase
				)			,						(
				    UpperCamelCase
				)			,						(
				    UpperCamelCase
				)			,						
				)						=  config_and_inputs

				UpperCamelCase						=  {
				    'input_ids': input_ids,
				    'head_mask': head_mask,
				}

				return config, inputs_dict






@require_tf
class        lowercase      (	_SCREAMING_SNAKE_CASE						,     _SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
		__lowercase : str			  =       (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
		__lowercase : Optional[Any]			  =       (TFXGLMForCausalLM,) if is_tf_available() else ()
		__lowercase : Any			  =       (
		    {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
		)
		__lowercase : str			  =       False
		__lowercase : Optional[Any]			  =       False
		__lowercase : Dict			  =       False



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  TFXGLMModelTester(self )
				UpperCamelCase						=  ConfigTester(self       ,   config_class=A_       ,   n_embd=37 )



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		@slow
		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase						=  TFXGLMModel.from_pretrained(A_ )
						self.assertIsNotNone(A_ )



		@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				super().test_resize_token_embeddings()






@require_tf
class        lowercase      (	unittest.TestCase     ):



		@slow
		def __UpperCamelCase				(				self       ,   A_=True )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
				UpperCamelCase						=  tf.convert_to_tensor([[2, 268, 9_865]]       ,   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
				UpperCamelCase						=  [2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
				# fmt: on
				UpperCamelCase						=  model.generate(A_       ,   do_sample=A_       ,   num_beams=1 )
				if verify_outputs:
						self.assertListEqual(output_ids[0].numpy().tolist()       ,   A_ )



		@slow
		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
				UpperCamelCase						=  TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )

				tf.random.set_seed(0 )
				UpperCamelCase						=  tokenizer('Today is a nice day and'       ,   return_tensors='tf' )
				UpperCamelCase						=  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' ):
						UpperCamelCase						=  model.generate(A_       ,   do_sample=A_       ,   seed=[7, 0] )
				UpperCamelCase						=  tokenizer.decode(output_ids[0]       ,   skip_special_tokens=A_ )

				UpperCamelCase						=  (
				    'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
				)
				self.assertEqual(A_       ,   A_ )



		@slow
		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
				UpperCamelCase						=  XGLMTokenizer.from_pretrained('facebook/xglm-564M' )

				UpperCamelCase						=  'left'

				# use different length sentences to test batching
				UpperCamelCase						=  [
				    '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',
				]

				UpperCamelCase						=  tokenizer(A_       ,   return_tensors='tf'       ,   padding=A_ )
				UpperCamelCase						=  inputs['input_ids']

				UpperCamelCase						=  model.generate(input_ids=A_       ,   attention_mask=inputs['attention_mask']       ,   max_new_tokens=12 )

				UpperCamelCase						=  tokenizer(sentences[0]       ,   return_tensors='tf' ).input_ids
				UpperCamelCase						=  model.generate(input_ids=A_       ,   max_new_tokens=12 )

				UpperCamelCase						=  tokenizer(sentences[1]       ,   return_tensors='tf' ).input_ids
				UpperCamelCase						=  model.generate(input_ids=A_       ,   max_new_tokens=12 )

				UpperCamelCase						=  tokenizer.batch_decode(A_       ,   skip_special_tokens=A_ )
				UpperCamelCase						=  tokenizer.decode(output_non_padded[0]       ,   skip_special_tokens=A_ )
				UpperCamelCase						=  tokenizer.decode(output_padded[0]       ,   skip_special_tokens=A_ )

				UpperCamelCase						=  [
				    '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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase      :				Union[str, Any]          = {
    "configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
    "processing_git": ["GitProcessor"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase      :				Dict          = [
					    "GIT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "GitForCausalLM",
					    "GitModel",
					    "GitPreTrainedModel",
					    "GitVisionModel",
					]

if TYPE_CHECKING:
					from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
					from .processing_git import GitProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_git import (
										    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GitForCausalLM,
										    GitModel,
										    GitPreTrainedModel,
										    GitVisionModel,
										)

else:
					import sys

					_UpperCAmelCase      :				int          = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
