


"""simple docstring"""







import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch
class 							lowerCamelCase       (			unittest.TestCase ):


							"""simple docstring"""






							@patch('''sys.argv'''		, ['''fakeprogrampath''', '''env''']				)
							def 					lowerCAmelCase     (						self :     Optional[Any]				):
													# test transformers-cli env
													import transformers.commands.transformers_cli

													with CaptureStd() as cs:
																			transformers.commands.transformers_cli.main()
													self.assertIn('''Python version'''		, cs.out				)
													self.assertIn('''Platform'''		, cs.out				)
													self.assertIn('''Using distributed or parallel set-up in script?'''		, cs.out				)






							@is_pt_tf_cross_test
							@patch(
							    '''sys.argv'''		, ['''fakeprogrampath''', '''pt-to-tf''', '''--model-name''', '''hf-internal-testing/tiny-random-gptj''', '''--no-pr''']				)
							def 					lowerCAmelCase     (						self :     int				):
													import transformers.commands.transformers_cli

													shutil.rmtree('''/tmp/hf-internal-testing/tiny-random-gptj'''		, ignore_errors=SCREAMING_SNAKE_CASE_				)  # cleans potential past runs
													transformers.commands.transformers_cli.main()

													# The original repo has no TF weights -- if they exist, they were created by the CLI
													self.assertTrue(os.path.exists('''/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5'''				)				)






							@require_torch
							@patch('''sys.argv'''		, ['''fakeprogrampath''', '''download''', '''hf-internal-testing/tiny-random-gptj''', '''--cache-dir''', '''/tmp''']				)
							def 					lowerCAmelCase     (						self :     str				):
													import transformers.commands.transformers_cli

													# # remove any previously downloaded model to start clean
													shutil.rmtree('''/tmp/models--hf-internal-testing--tiny-random-gptj'''		, ignore_errors=SCREAMING_SNAKE_CASE_				)

													# run the command
													transformers.commands.transformers_cli.main()

													# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
													self.assertTrue(os.path.exists('''/tmp/models--hf-internal-testing--tiny-random-gptj/blobs'''				)				)
													self.assertTrue(os.path.exists('''/tmp/models--hf-internal-testing--tiny-random-gptj/refs'''				)				)
													self.assertTrue(os.path.exists('''/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots'''				)				)






							@require_torch
							@patch(
							    '''sys.argv'''		, [
							        '''fakeprogrampath''',
							        '''download''',
							        '''hf-internal-testing/test_dynamic_model_with_tokenizer''',
							        '''--trust-remote-code''',
							        '''--cache-dir''',
							        '''/tmp''',
							    ]		, )
							def 					lowerCAmelCase     (						self :     Dict				):
													import transformers.commands.transformers_cli

													# # remove any previously downloaded model to start clean
													shutil.rmtree('''/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer'''		, ignore_errors=SCREAMING_SNAKE_CASE_				)

													# run the command
													transformers.commands.transformers_cli.main()

													# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
													self.assertTrue(os.path.exists('''/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs'''				)				)
													self.assertTrue(os.path.exists('''/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs'''				)				)
													self.assertTrue(
													    os.path.exists('''/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots'''				)				)



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a			 =   {
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   ["CamembertTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   ["CamembertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "CamembertForCausalLM",
						    "CamembertForMaskedLM",
						    "CamembertForMultipleChoice",
						    "CamembertForQuestionAnswering",
						    "CamembertForSequenceClassification",
						    "CamembertForTokenClassification",
						    "CamembertModel",
						    "CamembertPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFCamembertForCausalLM",
						    "TFCamembertForMaskedLM",
						    "TFCamembertForMultipleChoice",
						    "TFCamembertForQuestionAnswering",
						    "TFCamembertForSequenceClassification",
						    "TFCamembertForTokenClassification",
						    "TFCamembertModel",
						    "TFCamembertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert import CamembertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert_fast import CamembertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_camembert import (
												    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    CamembertForCausalLM,
												    CamembertForMaskedLM,
												    CamembertForMultipleChoice,
												    CamembertForQuestionAnswering,
												    CamembertForSequenceClassification,
												    CamembertForTokenClassification,
												    CamembertModel,
												    CamembertPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_camembert import (
												    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFCamembertForCausalLM,
												    TFCamembertForMaskedLM,
												    TFCamembertForMultipleChoice,
												    TFCamembertForQuestionAnswering,
												    TFCamembertForSequenceClassification,
												    TFCamembertForTokenClassification,
												    TFCamembertModel,
												    TFCamembertPreTrainedModel,
												)

else:
						import sys

						__a			 =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)