



import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


UpperCAmelCase							=       logging.get_logger(__name__)

class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   Optional[Any]       =       """semantic"""


							def __init__(				self	,	a_=10_000	,	a_=True	,	a_=768	,	a_=False	,	a_=False	,	a_=False	,	a_=False	,	a_=0.7	,	a_=True	,	a_=10_048	,	a_=129_595	,	a_=129_599	,	a_=10_000	,	a_=256	,	a_=49.9	,	**a_	,	):
											super().__init__(
											    temperature=a_	,	do_sample=a_	,	eos_token_id=a_	,	renormalize_logits=a_	,	max_new_tokens=a_	,	output_scores=a_	,	return_dict_in_generate=a_	,	output_hidden_states=a_	,	output_attentions=a_	,	**a_	,	)

											a__					   =		text_encoding_offset
											a__					   =		text_pad_token
											a__					   =		eos_token_id
											a__					   =		semantic_infer_token
											a__					   =		semantic_vocab_size
											a__					   =		max_input_semantic_length
											a__					   =		semantic_rate_hz




class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   Tuple       =       """coarse_acoustics"""


							def __init__(				self	,	a_=True	,	a_=False	,	a_=False	,	a_=False	,	a_=False	,	a_=0.7	,	a_=True	,	a_=12_048	,	a_=75	,	a_=2	,	a_=12_050	,	a_=256	,	a_ = 630	,	a_ = 60	,	**a_	,	):
											super().__init__(
											    temperature=a_	,	do_sample=a_	,	renormalize_logits=a_	,	output_scores=a_	,	return_dict_in_generate=a_	,	output_hidden_states=a_	,	output_attentions=a_	,	**a_	,	)

											a__					   =		coarse_semantic_pad_token
											a__					   =		coarse_rate_hz
											a__					   =		n_coarse_codebooks
											a__					   =		coarse_infer_token
											a__					   =		max_coarse_input_length
											a__					   =		max_coarse_history
											a__					   =		sliding_window_len




class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   str       =       """fine_acoustics"""


							def __init__(				self	,	a_=0.5	,	a_=512	,	a_=1_024	,	a_=8	,	**a_	,	):
											super().__init__(temperature=a_		)

											a__					   =		max_fine_history_length
											a__					   =		max_fine_input_length
											a__					   =		n_fine_codebooks




class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   Union[str, Any]       =       """bark"""
							UpperCamelCase__					:   str       =       True


							def __init__(				self	,	a_ = None	,	a_ = None	,	a_ = None	,	a_=24_000	,	a_=1_024	,	**a_	,	):
											if semantic_config is None:
															a__					   =		{}
															logger.info("""semantic_config is None. initializing the semantic model with default values."""		)

											if coarse_acoustics_config is None:
															a__					   =		{}
															logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""		)

											if fine_acoustics_config is None:
															a__					   =		{}
															logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""		)

											a__					   =		BarkSemanticGenerationConfig(**a_		)
											a__					   =		BarkCoarseGenerationConfig(**a_		)
											a__					   =		BarkFineGenerationConfig(**a_		)

											a__					   =		sample_rate
											a__					   =		codebook_size


							@classmethod
							def _a   (				cls	,	a_	,	a_	,	a_	,	**a_	,	):
											return cls(
											    semantic_config=semantic_config.to_dict()	,	coarse_acoustics_config=coarse_acoustics_config.to_dict()	,	fine_acoustics_config=fine_acoustics_config.to_dict()	,	**a_	,	)







							def _a   (				self		):
											a__					   =		copy.deepcopy(self.__dict__		)

											a__					   =		self.semantic_config.to_dict()
											a__					   =		self.coarse_acoustics_config.to_dict()
											a__					   =		self.fine_acoustics_config.to_dict()

											a__					   =		self.__class__.model_type
											return output


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      ):



     @patch('sys.argv'					,   ['fakeprogrampath', 'env']							)
     def       a__	(       self							):
            # 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       a__	(       self							):
            import transformers.commands.transformers_cli

            shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj'					,   ignore_errors=lowerCAmelCase__							)  # 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       a__	(       self							):
            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=lowerCAmelCase__							)

            # 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       a__	(       self							):
            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=lowerCAmelCase__							)

            # 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'							)							)