


import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()







def        SCREAMING_SNAKE_CASE_						(			__A :			Union[str, Any]  ,    __A :			Any  ,    __A :			Dict				)	->		Optional[Any]:

       """simple docstring"""

       return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]  ,    __A :			Dict  ,    __A :			Tuple  ,    __A :			Tuple="attention"				)	->		str:

       """simple docstring"""

       a_					:		List[Any]							=  np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]				)
       a_					:		int							=  k_tmp.reshape(k_tmp.shape[0]  ,    k_tmp.shape[1] * k_tmp.shape[2]				)
       a_					:		Optional[int]							=  np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]				)
       a_					:		Dict							=  o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]  ,    o_tmp.shape[2]				)
       a_					:		Any							=  np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]				)
       a_					:		Any							=  q_tmp.reshape(q_tmp.shape[0]  ,    q_tmp.shape[1] * q_tmp.shape[2]				)
       a_					:		List[str]							=  np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]				)
       a_					:		Optional[int]							=  v_tmp.reshape(v_tmp.shape[0]  ,    v_tmp.shape[1] * v_tmp.shape[2]				)
       return k, o, q, v







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]  ,    __A :			Optional[int]  ,    __A :			str  ,    __A :			List[Any]=False				)	->		int:

       """simple docstring"""

       if split_mlp_wi:
              a_					:		List[str]							=  params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
              a_					:		Union[str, Any]							=  params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
              a_					:		Any							=  (wi_a, wi_a)
       else:
              a_					:		List[str]							=  params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

       a_					:		Union[str, Any]							=  params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
       return wi, wo







def        SCREAMING_SNAKE_CASE_						(			__A :			Dict  ,    __A :			List[Any]  ,    __A :			str  ,    __A :			int				)	->		Tuple:

       """simple docstring"""

       return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]







def        SCREAMING_SNAKE_CASE_						(			__A :			dict  ,    *, __A :			int  ,    __A :			bool  ,    __A :			bool = False				)	->		Union[str, Any]:

       """simple docstring"""

       a_					:		Dict							=  traverse_util.flatten_dict(variables['target']				)
       a_					:		List[str]							=  {'/'.join(__A				): v for k, v in old.items()}

       # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
       a_					:		Optional[int]							=  'encoder/encoder/mlp/wi_0/kernel' in old
       print('Split MLP:'  ,    __A				)

       a_					:		List[str]							=  collections.OrderedDict()

       # Shared embeddings.
       a_					:		List[str]							=  old['token_embedder/embedding']

       # Encoder.
       for i in range(__A				):
              # Block i, layer 0 (Self Attention).
              a_					:		Tuple							=  tax_layer_norm_lookup(__A  ,    __A  ,    'encoder'  ,    'pre_attention_layer_norm'				)
              a_							,		a_							,		a_							,		a_					:		List[Any]							=  tax_attention_lookup(__A  ,    __A  ,    'encoder'  ,    'attention'				)
              a_					:		Optional[int]							=  layer_norm
              a_					:		Tuple							=  k.T
              a_					:		Tuple							=  o.T
              a_					:		Optional[Any]							=  q.T
              a_					:		List[str]							=  v.T

              # Block i, layer 1 (MLP).
              a_					:		List[Any]							=  tax_layer_norm_lookup(__A  ,    __A  ,    'encoder'  ,    'pre_mlp_layer_norm'				)
              a_							,		a_					:		Any							=  tax_mlp_lookup(__A  ,    __A  ,    'encoder'  ,    __A				)
              a_					:		Union[str, Any]							=  layer_norm
              if split_mlp_wi:
                     a_					:		Union[str, Any]							=  wi[0].T
                     a_					:		int							=  wi[1].T
              else:
                     a_					:		Tuple							=  wi.T
              a_					:		int							=  wo.T
              if scalable_attention:
                     # convert the rel_embedding of each layer
                     a_					:		Dict							=  tax_relpos_bias_lookup(
                         __A  ,    __A  ,    'encoder'				).T

       a_					:		Optional[Any]							=  old['encoder/encoder_norm/scale']

       if not scalable_attention:
              a_					:		int							=  tax_relpos_bias_lookup(
                  __A  ,    0  ,    'encoder'				).T
              a_					:		str							=  tax_relpos_bias_lookup(
                  __A  ,    0  ,    'decoder'				).T

       if not is_encoder_only:
              # Decoder.
              for i in range(__A				):
                     # Block i, layer 0 (Self Attention).
                     a_					:		str							=  tax_layer_norm_lookup(__A  ,    __A  ,    'decoder'  ,    'pre_self_attention_layer_norm'				)
                     a_							,		a_							,		a_							,		a_					:		Tuple							=  tax_attention_lookup(__A  ,    __A  ,    'decoder'  ,    'self_attention'				)
                     a_					:		int							=  layer_norm
                     a_					:		Optional[int]							=  k.T
                     a_					:		List[str]							=  o.T
                     a_					:		Any							=  q.T
                     a_					:		int							=  v.T

                     # Block i, layer 1 (Cross Attention).
                     a_					:		int							=  tax_layer_norm_lookup(__A  ,    __A  ,    'decoder'  ,    'pre_cross_attention_layer_norm'				)
                     a_							,		a_							,		a_							,		a_					:		str							=  tax_attention_lookup(__A  ,    __A  ,    'decoder'  ,    'encoder_decoder_attention'				)
                     a_					:		List[str]							=  layer_norm
                     a_					:		str							=  k.T
                     a_					:		Optional[Any]							=  o.T
                     a_					:		Tuple							=  q.T
                     a_					:		int							=  v.T

                     # Block i, layer 2 (MLP).
                     a_					:		Any							=  tax_layer_norm_lookup(__A  ,    __A  ,    'decoder'  ,    'pre_mlp_layer_norm'				)
                     a_							,		a_					:		int							=  tax_mlp_lookup(__A  ,    __A  ,    'decoder'  ,    __A				)
                     a_					:		int							=  layer_norm
                     if split_mlp_wi:
                            a_					:		List[Any]							=  wi[0].T
                            a_					:		List[str]							=  wi[1].T
                     else:
                            a_					:		int							=  wi.T
                     a_					:		List[str]							=  wo.T

                     if scalable_attention:
                            # convert the rel_embedding of each layer
                            a_					:		List[str]							=  tax_relpos_bias_lookup(__A  ,    __A  ,    'decoder'				).T

              a_					:		Dict							=  old['decoder/decoder_norm/scale']

              # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
              if "decoder/logits_dense/kernel" in old:
                     a_					:		Any							=  old['decoder/logits_dense/kernel'].T

       return new







def        SCREAMING_SNAKE_CASE_						(			__A :			List[Any]  ,    __A :			bool				)	->		List[Any]:

       """simple docstring"""

       a_					:		Union[str, Any]							=  collections.OrderedDict([(k, torch.from_numpy(v.copy()				)) for (k, v) in converted_params.items()]				)

       # Add what is missing.
       if "encoder.embed_tokens.weight" not in state_dict:
              a_					:		Dict							=  state_dict['shared.weight']

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     a_					:		int							=  state_dict['shared.weight']

              if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                     print('Using shared word embeddings as lm_head.'				)
                     a_					:		Any							=  state_dict['shared.weight']

       return state_dict







def        SCREAMING_SNAKE_CASE_						(			__A :			Dict  ,    __A :			int  ,    __A :			int  ,    __A :			List[str]  ,    __A :			int				)	->		int:

       """simple docstring"""

       a_					:		str							=  checkpoints.load_tax_checkpoint(__A				)
       a_					:		Optional[Any]							=  convert_tax_to_pytorch(
           __A  ,    num_layers=config.num_layers  ,    is_encoder_only=__A  ,    scalable_attention=__A				)
       a_					:		Dict							=  make_state_dict(__A  ,    __A				)
       model.load_state_dict(__A  ,    strict=__A				)







def        SCREAMING_SNAKE_CASE_						(			__A :			str  ,    __A :			Optional[Any]  ,    __A :			str  ,    __A :			bool = False  ,    __A :			bool = False  ,    )	->		Optional[Any]:

       """simple docstring"""

       a_					:		Dict							=  MTaConfig.from_json_file(__A				)
       print(F"""Building PyTorch model from configuration: {config}"""				)
       # Non-v1.1 checkpoints could also use T5Model, but this works for all.
       # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
       if is_encoder_only:
              a_					:		Tuple							=  UMTaEncoderModel(__A				)
       else:
              a_					:		Union[str, Any]							=  UMTaForConditionalGeneration(__A				)

       # Load weights from tf checkpoint
       load_tax_weights_in_ta(__A  ,    __A  ,    __A  ,    __A  ,    __A				)

       # Save pytorch-model
       print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
       model.save_pretrained(__A				)

       # Verify that we can load the checkpoint.
       model.from_pretrained(__A				)
       print('Done'				)


if __name__ == "__main__":
   UpperCAmelCase_      : Optional[int]  					=       argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
   # Required parameters
   parser.add_argument(
       '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
   )
   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   parser.add_argument(
       '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
   )
   parser.add_argument(
       '--scalable_attention',
       action='store_true',
       help='Whether the model uses scaled attention (umt5 model)',
       default=False,
   )
   UpperCAmelCase_      : List[Any]  					=       parser.parse_args()
   convert_tax_checkpoint_to_pytorch(
       args.tax_checkpoint_path,
       args.config_file,
       args.pytorch_dump_path,
       args.is_encoder_only,
       args.scalable_attention,
   )






import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
UpperCAmelCase_      : Dict  					=       {'LayoutLMv2Config', 'LayoutLMv3Config'}
@is_pipeline_test
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      snake_case__				:     List[str]        =						MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      snake_case__				:     Optional[Any]        =						TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

      if model_mapping is not None:
            snake_case__				:     str        =						{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
      if tf_model_mapping is not None:
            snake_case__				:     List[Any]        =						{
                config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
            }
      @require_torch
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Tuple:
             a_					:		List[Any]							=  pipeline(
                 task='text-classification'     ,							model='hf-internal-testing/tiny-random-distilbert'     ,							framework='pt'     )

             a_					:		int							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )

             a_					:		Tuple							=  text_classifier('This is great !'     ,							top_k=2     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}]     )

             a_					:		List[str]							=  text_classifier(['This is great !', 'This is bad']     ,							top_k=2     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                 ]     ,							)

             a_					:		Tuple							=  text_classifier('This is great !'     ,							top_k=1     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )

             # Legacy behavior
             a_					:		Union[str, Any]							=  text_classifier('This is great !'     ,							return_all_scores=SCREAMING_SNAKE_CASE__     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )

             a_					:		List[str]							=  text_classifier('This is great !'     ,							return_all_scores=SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[[{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}]]     )

             a_					:		int							=  text_classifier(['This is great !', 'Something else']     ,							return_all_scores=SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                 ]     ,							)

             a_					:		str							=  text_classifier(['This is great !', 'Something else']     ,							return_all_scores=SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[
                     {'label': 'LABEL_0', 'score': 0.504},
                     {'label': 'LABEL_0', 'score': 0.504},
                 ]     ,							)
      @require_torch
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			Dict:
             import torch

             a_					:		List[Any]							=  pipeline(
                 task='text-classification'     ,							model='hf-internal-testing/tiny-random-distilbert'     ,							framework='pt'     ,							device=torch.device('cpu'     )     ,							)

             a_					:		Any							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )
      @require_tf
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Optional[Any]:
             a_					:		List[str]							=  pipeline(
                 task='text-classification'     ,							model='hf-internal-testing/tiny-random-distilbert'     ,							framework='tf'     )

             a_					:		Optional[int]							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )
      @slow
      @require_torch
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Optional[int]:
             a_					:		List[str]							=  pipeline('text-classification'     )

             a_					:		Dict							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'POSITIVE', 'score': 1.0}]     )
             a_					:		Union[str, Any]							=  text_classifier('This is bad !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'NEGATIVE', 'score': 1.0}]     )
             a_					:		Tuple							=  text_classifier('Birds are a type of animal'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'POSITIVE', 'score': 0.988}]     )
      @slow
      @require_tf
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			List[Any]:
             a_					:		Dict							=  pipeline('text-classification'     ,							framework='tf'     )

             a_					:		Optional[Any]							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'POSITIVE', 'score': 1.0}]     )
             a_					:		int							=  text_classifier('This is bad !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'NEGATIVE', 'score': 1.0}]     )
             a_					:		Optional[int]							=  text_classifier('Birds are a type of animal'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'POSITIVE', 'score': 0.988}]     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     )		->			Any:
             a_					:		Optional[Any]							=  TextClassificationPipeline(model=SCREAMING_SNAKE_CASE__     ,							tokenizer=SCREAMING_SNAKE_CASE__     )
             return text_classifier, ["HuggingFace is in", "This is another test"]

      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			Union[str, Any]:
             a_					:		List[str]							=  text_classifier.model
             # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
             a_					:		Union[str, Any]							=  'HuggingFace is in'
             a_					:		int							=  text_classifier(SCREAMING_SNAKE_CASE__     )

             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}]     )
             self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()     )

             a_					:		Union[str, Any]							=  ['HuggingFace is in ', 'Paris is in France']
             a_					:		int							=  text_classifier(SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}, {'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}]     ,							)
             self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()     )
             self.assertTrue(outputs[1]['label'] in model.config.idalabel.values()     )

             # Forcing to get all results with `top_k=None`
             # This is NOT the legacy format
             a_					:		List[Any]							=  text_classifier(SCREAMING_SNAKE_CASE__     ,							top_k=SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  len(model.config.idalabel.values()     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[[{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}] * N, [{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}] * N]     ,							)

             a_					:		int							=  {'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
             a_					:		Optional[int]							=  text_classifier(SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}     ,							)
             self.assertTrue(outputs['label'] in model.config.idalabel.values()     )

             # This might be used a text pair, but tokenizer + pipe interaction
             # makes it hard to understand that it's not using the pair properly
             # https://github.com/huggingface/transformers/issues/17305
             # We disabled this usage instead as it was outputting wrong outputs.
             a_					:		Any							=  [['HuggingFace is in ', 'Paris is in France']]
             with self.assertRaises(SCREAMING_SNAKE_CASE__     ):
                    text_classifier(SCREAMING_SNAKE_CASE__     )

             # This used to be valid for doing text pairs
             # We're keeping it working because of backward compatibility
             a_					:		Tuple							=  text_classifier([[['HuggingFace is in ', 'Paris is in France']]]     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}]     ,							)
             self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()     )


