



import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
      from tests.trainer.test_trainer import (  # noqa
          RegressionModelConfig,
          RegressionPreTrainedModel,
          get_regression_trainer,
      )


set_seed(42)

UpperCamelCase_									=   get_tests_dir('''fixtures''')
UpperCamelCase_									=   os.path.join(dirname(get_tests_dir()))
UpperCamelCase_									=   dirname(os.path.abspath(__file__))

# default torch.distributed port
UpperCamelCase_									=   '''10999'''

UpperCamelCase_									=   '''t5-small'''

# *** Working Models ***
UpperCamelCase_									=   '''hf-internal-testing/tiny-albert'''
UpperCamelCase_									=   '''sshleifer/bart-tiny-random'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-bert'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-bigbird_pegasus'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-big_bird'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-blenderbot'''
UpperCamelCase_									=   '''bigscience/bigscience-small-testing'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-deberta'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-deberta-v2'''
UpperCamelCase_									=   '''sshleifer/tiny-distilbert-base-cased'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-electra'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-flaubert'''
UpperCamelCase_									=   '''stas/tiny-wmt19-en-de'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-funnel'''
UpperCamelCase_									=   '''sshleifer/tiny-gpt2'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-gptj'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-gpt_neo'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-layoutlm'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-led'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-longformer'''
UpperCamelCase_									=   '''stas/tiny-m2m_100'''  # hf tiny model is unsuitable
UpperCamelCase_									=   '''sshleifer/tiny-marian-en-de'''
UpperCamelCase_									=   '''sshleifer/tiny-mbart'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-mobilebert'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-mpnet'''
UpperCamelCase_									=   '''stas/pegasus-cnn_dailymail-tiny-random'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-prophetnet'''
UpperCamelCase_									=   '''sshleifer/tiny-distilroberta-base'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-squeezebert'''
UpperCamelCase_									=   '''patrickvonplaten/t5-tiny-random'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-t5-v1.1'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-vit'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-xlm-roberta'''
UpperCamelCase_									=   '''sshleifer/tiny-xlnet-base-cased'''


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-mt5'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-camembert'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-openai-gpt'''

# missing tokenizer files
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-convbert'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-layoutlmv2'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-hubert'''

# issues with tokenizer
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-ctrl'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-transfo-xl'''  # same as ctrl

# other issues with tiny models
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-ibert'''  # multiple issues with either mlm/qa/clas
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-reformer'''  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-dpr'''
# - "dpr"  examples/research_projects/rag-end2end-retriever/
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-rag'''
# - "rag" research_projects
UpperCamelCase_									=   ''''''
# - "luke" Entities classes - no plan to make such example
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-lxmert'''
# - "lxmert" doesn't work with run_qa.py
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-clip'''
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-speech_to_text'''
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-tapas'''







def       lowerCamelCase_  (    _a       :						Any=False    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Optional[Any]  		=					min(2   ,		get_gpu_count()    ) if distributed else 1
    UpperCAmelCase_     :       Optional[Any]  		=					os.environ.get("""DS_TEST_PORT"""   ,		_a    )
    return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()







def       lowerCamelCase_  (    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Dict  		=					F'''{FIXTURE_DIRECTORY}/tests_samples'''
    UpperCAmelCase_     :       Optional[int]  		=					F'''{data_dir_samples}/wmt_en_ro'''
    UpperCAmelCase_     :       Dict  		=					F'''{data_dir_samples}/xsum'''
    UpperCAmelCase_     :       Optional[Any]  		=					"""
        --do_train
        --max_train_samples 4
        --per_device_train_batch_size 2
        --num_train_epochs 1
        --fp16
        --report_to none
        --overwrite_output_dir
        """.split()

    # try to cover as many models as possible once (it's enough to run on one task per model)
    # but need a tiny model for each
    #
    # should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
    UpperCAmelCase_     :       List[Any]  		=					{
        """trans""": [
            """bart""",
            """fsmt""",
            """m2m_100""",
            """marian""",
            """mbart""",
            """t5""",
            """t5_v1""",
            # "mt5", missing model files
        ],
        """sum""": [
            """pegasus""",
        ],
        """clm""": [
            """big_bird""",
            """bigbird_pegasus""",
            """blenderbot""",
            """bloom""",
            """gpt2""",
            """gpt_neo""",
            """gptj""",
            """xlm-roberta""",
            """prophetnet""",
            # "camembert", missing model files
        ],
        """mlm""": [
            """albert""",
            """deberta""",
            """deberta-v2""",
            """distilbert""",
            """electra""",
            """flaubert""",
            """funnel""",
            """layoutlm""",
            # "reformer", # multiple issues with either mlm/qa/clas
        ],
        """qa""": [
            """led""",
            """longformer""",
            """mobilebert""",
            """mpnet""",
            """roberta""",
            """squeezebert""",
            # "convbert", # missing tokenizer files
            # "layoutlmv2", missing model files
        ],
        """clas""": [
            """bert""",
            """xlnet""",
            # "hubert", # missing tokenizer files
            # "ibert", # multiple issues with either mlm/qa/clas
            # "transfo-xl", # tokenizer issues as ctrl
            # "ctrl", # tokenizer issues
            # "openai-gpt", missing model files
            # "tapas", multiple issues
        ],
        """img_clas""": [
            """vit""",
        ],
    }

    UpperCAmelCase_     :       Union[str, Any]  		=					F'''{ROOT_DIRECTORY}/examples/pytorch'''

    UpperCAmelCase_     :       List[str]  		=					{
        """trans""": F'''
        {scripts_dir}/translation/run_translation.py
        --train_file {data_dir_wmt}/train.json
        --source_lang en
        --target_lang ro
        ''',
        """sum""": F'''
        {scripts_dir}/summarization/run_summarization.py
        --train_file {data_dir_xsum}/sample.json
        --max_source_length 12
        --max_target_length 12
        --lang en
        ''',
        """clm""": F'''
        {scripts_dir}/language-modeling/run_clm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        --block_size 8
        ''',
        """mlm""": F'''
        {scripts_dir}/language-modeling/run_mlm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        ''',
        """qa""": F'''
        {scripts_dir}/question-answering/run_qa.py
        --train_file {data_dir_samples}/SQUAD/sample.json
        ''',
        """clas""": F'''
        {scripts_dir}/text-classification/run_glue.py
        --train_file {data_dir_samples}/MRPC/train.csv
        --max_seq_length 12
        --task_name MRPC
        ''',
        """img_clas""": F'''
        {scripts_dir}/image-classification/run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --remove_unused_columns False
            --max_steps 10
            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json
        ''',
    }

    UpperCAmelCase_     :       List[Any]  		=					get_launcher(distributed=_a    )

    UpperCAmelCase_     :       Tuple  		=					{}
    for task, args in tasks.items():
        UpperCAmelCase_     :       Optional[int]  		=					args.split()
        for model in tasksamodels[task]:
            UpperCAmelCase_     :       Optional[Any]  		=					globals()[F'''{model.upper().replace('-'   ,		'_'    )}_TINY''']
            UpperCAmelCase_     :       str  		=					F'''--model_name_or_path {model_name}'''.split()
            UpperCAmelCase_     :       Optional[Any]  		=					launcher + args + args_model + args_main

            # # generation special case
            # if task == "gen":
            #     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
            #     args_model += f"--model_type {model}".split()
            #     cmds[f"{task}_{model}"] = launcher + args + args_model
            # else:

    return cmds


UpperCamelCase_									=   make_task_cmds()

UpperCamelCase_									=   '''zero2'''
UpperCamelCase_									=   '''zero3'''

UpperCamelCase_									=   [ZEROa, ZEROa]







def       lowerCamelCase_  (    _a       :						List[str]   ,		_a       :						int   ,		_a       :						List[Any]    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Optional[Any]  		=					parameterized.to_safe_name("""_""".join(str(_a    ) for x in param.args    )    )
    return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
UpperCamelCase_									=   list(itertools.product(stages, task_cmds.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   def       A__    (    self:     Any  ,lowerCamelCase_:     int  ,lowerCamelCase_:     int							)	->      int:
       # return a ready to run train cmd
       if task not in task_cmds:
           raise ValueError(F'''don\'t know of task {task}, have {task_cmds.keys()}'''							)

       UpperCAmelCase_     :       List[Any]  		=					task_cmds[task]
       UpperCAmelCase_     :       Union[str, Any]  		=					F'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

       UpperCAmelCase_     :       int  		=					self.get_auto_remove_tmp_dir()
       UpperCAmelCase_     :       str  		=					F'''--output_dir {output_dir}'''.split()

       cmd += args_ds + args_out

       return cmd, output_dir






   @parameterized.expand(lowerCamelCase_  ,name_func=lowerCamelCase_							)
   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     Optional[int]  ,lowerCamelCase_:     str							)	->      Tuple:
       # testing the ability to do a run followed by recovery of full fp32 weights

       UpperCAmelCase_		, UpperCAmelCase_     :       int  		=					self.get_task_cmd(lowerCamelCase_  ,lowerCamelCase_							)

       # 1. generate the checkpoint
       cmd += "--save_steps 1".split()
       # keep for quick debug
       # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
       execute_subprocess_async(lowerCamelCase_  ,env=self.get_env()							)

       # 2. test that the fp32 weights get reconsolidated
       UpperCAmelCase_     :       str  		=					F'''{output_dir}/checkpoint-1'''
       UpperCAmelCase_     :       Optional[int]  		=					F'''{chkpt_dir}/out.bin'''
       UpperCAmelCase_     :       int  		=					F'''{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'''
       # keep for quick debug
       # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
       subprocess.check_call(lowerCamelCase_  ,shell=lowerCamelCase_							)
       assert os.path.exists(lowerCamelCase_							), F'''{recovered_model_path} was not found'''

       # possibly could also test that the resulting saved model is usable but given that we use
       # random models we won't know if it's any good


import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
__a							  =						logging.get_logger(__name__)

__a							  =						{
    """post_extract_proj""": """feature_projection.projection""",
    """models.0.layer_norm""": """feature_projection.layer_norm""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
__a							  =						[
    """lm_head""",
]

def    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_  ,				a_		)     ->Dict:
		for attribute in key.split("."		):
				A				=getattr(a_  ,				a_		)

		if weight_type is not None:
				A				=getattr(a_  ,				a_		).shape
		else:
				A				=hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
				    f''' {value.shape} for {full_name}'''		)

		if weight_type == "weight":
				A				=value
		elif weight_type == "weight_g":
				A				=value
		elif weight_type == "weight_v":
				A				=value
		elif weight_type == "bias":
				A				=value
		else:
				A				=value

		logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''		)

def    UpperCamelCase_ (	a_  ,				a_  ,				a_		)     ->Union[str, Any]:
		A				=[]
		A				=fairseq_model.state_dict()

		if not is_headless:
				A				=hf_model.dataavec_audio.feature_extractor
				A				=hf_model.dataavec_audio.encoder.pos_conv_embed

		else:
				A				=hf_model.feature_extractor
				A				=hf_model.encoder.pos_conv_embed

		for name, value in fairseq_dict.items():
				A				=False
				if "conv_layers" in name:
						load_conv_layer(
						    a_  ,				a_  ,				a_  ,				a_  ,				)
						A				=True
				elif "pos_conv" in name:
						load_pos_conv_layer(
						    a_  ,				a_  ,				a_  ,				a_  ,				)
						A				=True
				else:
						for key, mapped_key in MAPPING.items():
								if not is_headless:
										A				="data2vec_audio." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split("w2v_model."		)[-1] == name.split("."		)[0]:
										A				=True
										if "*" in mapped_key:
												A				=name.split(a_		)[0].split("."		)[-2]
												A				=mapped_key.replace("*"  ,				a_		)
										if "weight_g" in name:
												A				="weight_g"
										elif "weight_v" in name:
												A				="weight_v"
										elif "bias" in name:
												A				="bias"
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												A				="weight"
										else:
												A				=None
										set_recursively(a_  ,				a_  ,				a_  ,				a_  ,				a_		)
								continue
				if not is_used:
						unused_weights.append(a_		)

		logger.warning(f'''Unused weights: {unused_weights}'''		)

def    UpperCamelCase_ (	a_  ,				a_		)     ->List[str]:
		A				=path.split("."		)
		return reduce(a_  ,				a_  ,				a_		)

def    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_		)     ->str:
		A				=access_by_string(a_  ,				a_		)
		A				=hf_weight.data

		if fsq_value.shape != hf_value.shape:
				raise ValueError(f'''{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'''		)
		A				=fsq_value
		logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''		)

def    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_		)     ->List[Any]:
		A				=full_name.split("conv_layers."		)[-1]
		A				=name.split("."		)
		A				=int(items[0]		)
		A				=int(items[1]		)

		A				=name.split("."		)[-1]
		if type_id == 0:
				A				="conv"
		elif type_id == 2:
				A				="layer_norm"
		else:
				unused_weights.append(a_		)
				return

		set_weights(a_  ,				a_  ,				a_  ,				f'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''		)

def    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_		)     ->str:
		A				=full_name.split("pos_conv."		)[-1]
		A				=name.split("."		)
		A				=int(items[0]		)
		A				=int(items[1]		)

		A				=name.split("."		)[-1]
		if type_id != 0:
				unused_weights.append(a_		)
				return
		else:
				A				="conv"

		set_weights(a_  ,				a_  ,				a_  ,				f'''layers.{layer_id}.{layer_type}.{weight_type}'''		)

@torch.no_grad()
def    UpperCamelCase_ (	a_  ,				a_  ,				a_=None  ,				a_=None  ,				a_=True		)     ->Optional[int]:
		if config_path is not None:
				A				=DataaVecAudioConfig.from_pretrained(a_		)
		else:
				A				=DataaVecAudioConfig()

		if not is_finetuned:
				# Modify final_proj layer name
				A				=DataaVecAudioModel(a_		)
				A				=os.path.dirname(a_		)

				A				=torch.load(a_		)
				A				=state_dict["model"].pop("final_proj.0.weight"		)
				A				=state_dict["model"].pop("final_proj.0.bias"		)
				A				=os.path.join(a_  ,				"converted.pt"		)
				torch.save(a_  ,				a_		)
		else:
				A				=DataaVecAudioForCTC(a_		)
				A				=checkpoint_path

		def load_dataavec(a_		):
				A			,    A			,    A				=fairseq.checkpoint_utils.load_model_ensemble_and_task([path]		)
				return model[0].eval()

		A				=load_dataavec(a_		)

		recursively_load_weights(a_  ,				a_  ,				not is_finetuned		)

		A				=WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"		)

		A				=load_dataset("patrickvonplaten/librispeech_asr_dummy"  ,				"clean"  ,				split="validation"		)
		A				=[x["array"] for x in ds[:4]["audio"]]

		A				=processor(a_  ,				return_tensors="pt"  ,				padding=a_		)

		A				=inputs.input_values
		A				=inputs.attention_mask
		#    input_values = inputs.input_values[:, :-1]
		#    attention_mask = inputs.attention_mask[:, :-1]

		hf_wavavec.eval()
		model.eval()
		if is_finetuned:
				A				=model(source=a_  ,				padding_mask=(1 - attention_mask)  ,				mask=a_  ,				features_only=a_		)[
				    "encoder_out"
				].transpose(0  ,				1		)
				A				=hf_wavavec(a_  ,				attention_mask=a_		)["logits"]

				A				=torch.argmax(a_  ,				dim=-1		)
				A				=processor.batch_decode(a_		)

				print(f'''Expected Output: {ds[:4]["text"]}, Pred: {output_string}'''		)
		else:
				A				=model(source=a_  ,				padding_mask=(1 - attention_mask)  ,				mask=a_  ,				features_only=a_		)[
				    "layer_results"
				][-1][0].transpose(0  ,				1		)
				A				=hf_wavavec(a_  ,				attention_mask=a_		)["last_hidden_state"]

		print(our_output.shape  ,				their_output.shape		)
		A				=torch.max(torch.abs(our_output - their_output		)		).item()
		print(f'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-7
		A				=torch.allclose(a_  ,				a_  ,				atol=1E-3		)
		print("Do both models output the same tensors?"  ,				"🔥" if success else "💩"		)
		if not success:
				raise Exception("Something went wRoNg"		)

		hf_wavavec.save_pretrained(a_		)

		if is_finetuned:
				processor.save_pretrained(a_		)
		else:
				processor.feature_extractor.save_pretrained(a_		)


if __name__ == "__main__":
	__a							  =						argparse.ArgumentParser()
	parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
	parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
	parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
	parser.add_argument(
	    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
	)
	__a							  =						parser.parse_args()
	convert_wavaveca_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)




